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Today, genomic prediction (GP) is an established technology in plant and animal breeding programs. Current standard methods are purely based on statistical considerations but do not make use of the abundant biological knowledge, which is easily available from public databases. Major questions that have to be answered before biological prior information can be used routinely in GP approaches are which types of information can be used, and at which points they can be incorporated into prediction methods. In this study, we propose a novel strategy to incorporate gene annotation into GP of complex phenotypes by defining haploblocks according to gene positions. Haplotype effects are then modeled as categorical or as numerical allele dosage variables. The underlying concept of this approach is to build the statistical model on variables representing the biologically functional units. We evaluate the new methods with data from a heterogeneous stock mouse population, the (), and a rice breeding population from the Rice Diversity Panel. Our results show that using gene annotation to define haploblocks often leads to a comparable, but for some traits to a higher, predictive ability compared to SNP-based models or to haplotype models that do not use gene annotation information. Modeling gene interaction effects can further improve predictive ability. We also illustrate that the additional use of markers that have not been mapped to any gene in a second separate relatedness matrix does in many cases not lead to a relevant additional increase in predictive ability when the first matrix is based on haploblocks defined with gene annotation data, suggesting that intergenic markers only provide redundant information on the considered data sets. Therefore, gene annotation information seems to be appropriate to perceive the importance of DNA segments. Finally, we discuss the effects of gene annotation quality, marker density, and linkage disequilibrium on the performance of the new methods. To our knowledge, this is the first work that incorporates epistatic interaction or gene annotation into haplotype-based prediction approaches.

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BackgroundGenome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence (WGS) data. However, sequencing thousands of individuals of interest is expensive. Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide insight into the design and execution of genotype imputation.ResultsWe genotyped 450 chickens with a 600 K SNP array, and sequenced 24 key individuals by whole genome re-sequencing. Accuracy of imputation from putative 60 K and 600 K array data to WGS data was 0.620 and 0.812 for Beagle, and 0.810 and 0.914 for FImpute, respectively. By increasing the sequencing cost from 24X to 144X, the imputation accuracy increased from 0.525 to 0.698 for Beagle and from 0.654 to 0.823 for FImpute. With fixed sequence depth (12X), increasing the number of sequenced animals from 1 to 24, improved accuracy from 0.421 to 0.897 for FImpute and from 0.396 to 0.777 for Beagle. Using optimally selected key individuals resulted in a higher imputation accuracy compared with using randomly selected individuals as a reference population for re-sequencing. With fixed reference population size (24), imputation accuracy increased from 0.654 to 0.875 for FImpute and from 0.512 to 0.762 for Beagle as the sequencing depth increased from 1X to 12X. With a given total cost of genotyping, accuracy increased with the size of the reference population for FImpute, but the pattern was not valid for Beagle, which showed the highest accuracy at six fold coverage for the scenarios used in this study.ConclusionsIn conclusion, we comprehensively investigated the impacts of several key factors on genotype imputation. Generally, increasing sequencing cost gave a higher imputation accuracy. But with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS. An optimal imputation strategy should take size of reference population, imputation algorithms, marker density, and population structure of the target population and methods to select key individuals into consideration comprehensively. This work sheds additional light on how to design and execute genotype imputation for livestock populations.Electronic supplementary materialThe online version of this article (10.1186/s40104-018-0241-5) contains supplementary material, which is available to authorized users.

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Bioactivity-guided isolation of the endophytic fungus Fusarium sambucinum TE-6L residing in Nicotiana tabacum L. led to the discovery of two new angularly prenylated indole alkaloids (PIAs) with pyrano[2,3-g]indole moieties, amoenamide C (1) and sclerotiamide B (2), and four known biosynthetic congeners (3−6). Their structures were determined by comprehensive spectroscopic techniques, electronic circular dichroism (ECD), and X-ray diffraction. Compound 1 containing the bicyclo[2.2.2]diazaoctane core and indoxyl unit is rarely reported. All the compounds were evaluated for their antimicrobial and insecticidal activities. Notably, compounds 1−3 showed potent inhibitory effects against three human-and one plantpathogenic bacterium, and seven plant-pathogenic fungi. Compounds 2−4 also exhibited remarkable larvicidal activity against first instar larvae of the cotton bollworm Helicoverpa armigera with mortality rates of 70.2%, 83.2%, and 70.5%, respectively. Further toxicity tests on zebrafish embryos were performed to evaluate the potential toxicity of PIAs. Of significance was that compound 3 in particular exhibited the highest activities but the lowest effects on the hatching of embryos among all the compounds. This study provides a basis for understanding developmental toxicity of PIAs exposure to zebrafish embryos, and also indicates the potential environmental risks of other natural compounds exposure in the aquatic ecosystem.